Dyballa, LucianoLang, SamuelHaslund-Gourley, AlexandraYemini, EviatarZucker, Steven W2024-05-232024-05-232024-02-27Dyballa L, Lang S, Haslund-Gourley A, Yemini E, Zucker SW. Learning dynamic representations of the functional connectome in neurobiological networks. ArXiv [Preprint]. 2024 Feb 27:arXiv:2402.14102v2. PMID: 38463505; PMCID: PMC10925416.2331-842210.48550/arXiv.2402.1410238463505https://hdl.handle.net/20.500.14038/53357This article is a preprint. Preprints are preliminary reports of work that have not been certified by peer review.The static synaptic connectivity of neuronal circuits stands in direct contrast to the dynamics of their function. As in changing community interactions, different neurons can participate actively in various combinations to effect behaviors at different times. We introduce an unsupervised approach to learn the dynamic affinities between neurons in live, behaving animals, and to reveal which communities form among neurons at different times. The inference occurs in two major steps. First, pairwise non-linear affinities between neuronal traces from brain-wide calcium activity are organized by non-negative tensor factorization (NTF). Each factor specifies which groups of neurons are most likely interacting for an inferred interval in time, and for which animals. Finally, a generative model that allows for weighted community detection is applied to the functional motifs produced by NTF to reveal a dynamic functional connectome. Since time codes the different experimental variables (e.g., application of chemical stimuli), this provides an atlas of neural motifs active during separate stages of an experiment (e.g., stimulus application or spontaneous behaviors). Results from our analysis are experimentally validated, confirming that our method is able to robustly predict causal interactions between neurons to generate behavior.enThe original copyright holder retains ownership after posting on arXiv. This preprint is made available under a Creative Commons Attribution 4.0 International license.; Attribution 4.0 Internationalhttp://creativecommons.org/licenses/by/4.0/Neurons and CognitionMachine LearningSocial and Information NetworksPreprintArXiv